Meta-modeling environments allow modelers to simulate complex scenarios with high-level modeling tools. Meta-modeling environments provide the user with the basic tools with which the user can create a meta-model. The user-defined meta-model, which may also be referred to as an ontology, can then be processed by the meta-modeling environment to generate an interface that can be used to create one or more instance models. Often, meta-modeling environments provide a visual language, allowing modelers to create detailed models, without requiring low-level knowledge of the underlying classes that make up the model.
Existing meta-modeling environments typically may be used to create domain-specific modeling tools. Meta-models include syntax, semantics and entities. Entities such as routers, switches, operating systems, VMs, Servers continuously generate vast amount of logs data per second. This data contain useful information which can be used to take automatic action if machine can understand it. Automation tools can perform troubleshooting, security check if this unstructured information can be converted into structured format. The input information such as log files or any other data sources can be straightforward or obscure, depending on the attitude of the developer who wrote them. Either way, most of the time they are written with human readers in mind. It is necessary to extract relevant information from the data.
Information extraction is task to extract domain specific relevant information from different data sources. Extracted information can be domain entities, association between entities, attributes like hostname, port number, data etc. and associated verbs with each entity. Extracting information can be from logs, html files, pdf files, domain corpus, webs and scanned images of forms. The extraction of data from speech data and other data sources is also addressed.
In the existing solutions, a generic unified approach is missing for creating instance of a model for infrastructure, forms, invoice, purchase orders, goods received notes, clinical trials, processes etc. and associating them with existing instance models to do analysis especially in the field of forecasting, healthcare, compliance, diagnostic, automation etc. In addition to that, there are problems related to unstructured message, entity mapping problems, attribute mapping problem, and artificial ignorance problem etc.